Campus Undergraduate Summer Internship Program - 2027 Data Analytics, Enterprise Technology Services- Palo Alto, CA

Business Unit / Role Specific Info

The Enterprise Technology Services organization partners with every part of the American Express business to power the company’s growth and innovation with trust and efficiency, and drive competitive differentiation with speed. We support the delivery and operations of technology, digital, and data capabilities, platforms, and services globally. Specifically, our team is responsible for the company’s technology engineering, architecture, and infrastructure, providing 24x7 support to ensure an uninterrupted, high-quality experience for customers and colleagues. We also provide product management for core enterprise platforms, and lead technology risk and information security, enterprise data governance and platforms, digital product and design, and enterprise AI platforms on behalf of the company.

At American Express, we empower future data professionals to learn, innovate, and make an impact from day one. As a Data and Analytics Intern in Enterprise Technology Services, you will join a 10 week Summer Internship Program and support analytics work that helps technology teams make informed decisions across governance, architecture, modeling, data science, and emerging technology initiatives.

This role is designed for students interested in using data, analytics, financial insight, modeling, AI, or quantitative methods to solve business and technology problems. Depending on team alignment, you may work with technology business enablement, governance, model focused teams, enterprise business data architecture, data science teams, or quantum computing exploration efforts.

Potential Focus Areas

American Express Data and Analytics Interns may be aligned to different technology teams based on business needs, project requirements, and individual strengths. Experience in one or more of the following areas is beneficial:

• Technology Business Enablement: portfolio analysis, financial management, delivery analytics, resource insights, operating rhythm materials, executive reporting, or business performance analysis.
• Actuarial, Modeling, and AI Governance Analytics: quantitative analysis, actuarial methods, model documentation, model output review, scenario analysis, model governance, AI oversight, or responsible AI concepts.
• Enterprise Business Data Architecture: data requirements and data-source analysis, source-to-target mapping, metadata and lineage, data quality and controls, data governance and standards, and foundational enterprise data architecture concepts.
• Data Science: Python, R, SQL, exploratory analysis, statistical analysis, predictive modeling fundamentals, evaluation metrics, feature review, visualization, or insight generation.
• Quantum Computing Exploration: Quantum computing fundamentals, emerging technology research, use case evaluation, experimentation documentation, technical landscape analysis, or early stage analytics.
• Core Skills Across All Areas: analytical thinking, attention to detail, communication, collaboration, intellectual curiosity, responsible use of data and AI, and ability to explain insights to technical and non technical audiences.

Responsibilities and What Type of Work to Expect

• Collect, clean, validate, and organize data from technology, portfolio, financial, operational, architectural, or modeling sources to support analysis and reporting.
• Support data requirements, source-to-target mapping, metadata and lineage documentation, and data quality checks or control validation for assigned projects.
• Analyze datasets to identify trends, anomalies, opportunities, risks, and insights relevant to technology and business decision making.
• Support dashboards, key performance indicators, governance reports, executive summaries, and stakeholder ready presentations.
• Assist with financial, statistical, quantitative, exploratory, or scenario based analyses based on team placement and project needs.
• Document assumptions, data sources, requirements, mappings, calculations, methodology, metadata, lineage, data quality and control considerations, and analytical outputs to support transparency, traceability, and reproducibility.
• Partner with technology, product, finance, architecture, risk, data science, and business stakeholders to understand requirements and translate them into clear analytical outputs.
• Use AI enabled analytics, productivity, and reporting tools to support research, summarization, data exploration, and workflow efficiency while validating outputs before use.
• Communicate findings clearly to technical and non technical audiences through written summaries, presentations, dashboards, or discussion materials.

Minimum Qualifications

• Currently enrolled in a full time Bachelor’s degree program in Business Administration, Finance, Economics, Mathematics, Statistics, Actuarial Science, Data Analytics, Information Systems, Computer Science, Engineering, or a related discipline.
• Interest in one or more areas such as data analytics, technology business enablement, data architecture, actuarial analytics, model governance, data science, AI, quantum computing, or business intelligence.
• Foundational knowledge of data analytics concepts, including data collection, validation, analysis, visualization, interpretation, and insight generation.
• Foundational understanding of financial analysis, statistics, quantitative analysis, risk analysis, data modeling, or business performance measurement.
• Awareness of Software Development Lifecycle, Agile methodology, data governance, or technology delivery concepts.
• Foundational understanding of Generative AI concepts, responsible use, prompt based workflows, and human validation of AI generated outputs.
• Strong analytical thinking, attention to detail, communication, organization, problem solving, and collaboration skills.

Preferred Qualifications

• Bachelor’s degree candidates with an expected graduation date between December 2027 and June 2028.
• Coursework, projects, research, student organizations, or internship experience related to data analytics, finance, actuarial science, quantitative modeling, data science, business analysis, architecture, AI, or emerging technologies.
• Coursework, projects, research, or internship exposure to data requirements, source-to-target mapping, data quality and controls, metadata, lineage, or data governance.
• Experience using analytical, reporting, or presentation tools such as Excel, PowerPoint, SQL, Python, R, SAS, Tableau, Power BI, or similar platforms.
• Exposure to financial modeling, dashboard development, statistical analysis, scenario analysis, model documentation, data quality review, metadata, or data lineage concepts.
• Interest in AI governance, model risk management, responsible AI, explainability, enterprise data architecture, predictive analytics, or quantum computing research.
• Familiarity with project, portfolio, or workflow tools such as Jira, Rally, Confluence, SharePoint, Microsoft Project, or related platforms.
• Curiosity about Agentic AI reporting, AI enabled analytics, productivity tools, automated insight generation, and responsible validation of AI generated recommendations.
• Ability to build clear narratives from data, communicate findings clearly, and work effectively across finance, technology, architecture, risk, product, and business teams.

Our team reviews applications on a rolling basis. We appreciate your patience while we consider your application and will contact qualified candidates regarding next steps.

Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions.

Ideal Candidate Profile

We are seeking curious and analytical students who enjoy working with data, solving complex problems, and learning how analytics can support responsible technology decisions. Successful candidates will combine quantitative thinking, business curiosity, strong communication skills, and a commitment to accuracy, integrity, and continuous learning.